Existing computer-implemented recommender systems can provide personalized recommendations basis a determination of the expected interests of recommendation recipients from the behavioral history with respect to a system. However, interest levels alone may be inadequate in some cases for generating the most beneficial recommendations. Thus there is a need for a system and method that incorporates information beyond just inferred interests in generating recommendations.